|
on Efficiency and Productivity |
| By: | Subhash C. Ray (University of Connecticut); Linge Yang (University of Connecticut) |
| Abstract: | In this paper, we use Data Envelopment Analysis (DEA) to measure profit efficiency in wheat farming, drawing on farm-level data from 4, 529 farms across eight major wheat-producing states in India for the year 2016–2017. To the best of our knowledge, this is the first study to examine production efficiency in Indian agriculture focused on a single crop rather than the total value of output at a national scale, encompassing all major wheat-growing regions. We decompose profit efficiency into its technical and allocative components by exploring several alternative approaches to measure technical efficiency, including McFadden’s gauge function along with the conventional radial input-oriented distance function and the directional distance function. To explicitly account for agro-climatic heterogeneity across states, we construct state-specific production frontiers using only within-state observations to define the reference technology. Our empirical findings reveal that while average technical efficiency is not particularly low, extremely low levels of allocative efficiency significantly reduce overall profit efficiency. This result holds consistently across all definitions of technical efficiency considered. We speculate that the measured allocative efficiency could have resulted from market imperfections. |
| Keywords: | Technical Efficiency; Allocative Efficiency; Nerlovian Efficiency; Gauge Function; Short Run Variable Profit Function; Data Envelopment Analysis |
| JEL: | C6 Q12 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:uct:uconnp:2025-11 |
| By: | Mamun, Abdullah |
| Abstract: | The agriculture sector receives substantial fiscal subsidies in various forms, including through programs that are linked to production and others that are decoupled. As the sector has reached the technology frontier in production over the last three decades or so, particularly in high- and middle-income countries, it is intriguing to investigate the impact of subsidies on productivity at aggregate level. This study examines the impact of subsidies on productivity growth in agriculture globally using a long time series on the nominal rate of assistance for 42 countries that covers over 80 percent of agricultural production. The econometric results show heterogenous effects from various subsidy instruments depending on the choice of productivity measure. Regression results suggest a strong positive effect of input subsidies on both output growth and labor productivity. A positive but relatively small impact of output subsidies is found on output growth only. Subsidies that are mostly decoupled reveal no significant impact on any of the productivity measures. |
| Keywords: | agricultural productivity; agricultural technology; econometrics; globalization; input output analysis; subsidies |
| Date: | 2024–03–28 |
| URL: | https://d.repec.org/n?u=RePEc:fpr:ifprid:140668 |
| By: | Holger Görg, Toshihiro Okubo, Eric Strobl, Maximilian von Ehrlich |
| Abstract: | In this paper we use comprehensive historic firm level data for 1925 to 1938 to estimate productivity spillovers from Japanese textile companies’ affiliates in China (Zaikabo) to local cotton producers in China. We geo-localized firms in order to capture the important role of distance in facilitating productivity spillovers. Our results provide clear evidence for positive productivity spillovers from Zaikabo to local Chinese firms. This goes hand-in-hand with a change in production technology towards greater use of capital (spindles). We also find that spillovers are very localised, being strongest within a radius of up to 10km around the Zaikabo. Furthermore, evidence for spillovers is particularly strong for firms in Shanghai. Our paper is the first to provide evidence for such spillovers from foreign firms in a historical context. |
| JEL: | F23 N65 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:ube:dpvwib:dp2506 |
| By: | Behn, Markus; Reghezza, Alessio |
| Abstract: | This paper examines the relationship between capital requirements, capital ratios and bank competitiveness – measured as profit efficiency. Using data envelopment analysis techniques, profit efficiency scores were estimated for a sample of listed significant institutions directly supervised by the European Central Bank. In calculating the scores, use was made of rich supervisory data on bank-specific characteristics and capital requirements, in addition to macroeconomic variables. The findings revealed that capital requirements do not have a statistically significant effect on profit efficiency. The insignificant relationship also held true when capital requirements were broken down into microprudential and macroprudential requirements. For capital ratios, the relationship with profit efficiency was linearly statistically insignificant, but did display a statistically significant non-linear relationship that followed an inverted U-shape: profit efficiency rose with capital up to a threshold (estimated at a common equity tier 1 ratio of around 18%), after which further increases curbed profit efficiency. These findings were robust to a wide battery of robustness checks, including an extension of the sample to unlisted banks and the use of different efficiency measures and of various methods to control for confounding factors. These results underscore the need for policymakers to ensure that banks remain resilient, maintain strong capital ratios and manage risk well. In addition, they point to the intricate link between bank capital, regulation and competitiveness, contributing to the ongoing debate about the European banking sector’s ability to support economic growth and innovation. JEL Classification: G21, G28 |
| Keywords: | bank profits, capital buffers, financial stability, macroprudential policy |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbops:2025376 |
| By: | Marie Briere; Léopold Simar; Ariane Szafarz; Anne Vanhems |
| Abstract: | This paper presents a novel performance test for investment portfolios by constructing bootstrap confidence intervals around the distance to the efficient frontier of risky assets. Using a general input-output framework, with outputs like return and skewness, and inputs such as variance and kurtosis, our distance measure quantifies efficiency loss relative to a personalized efficient benchmark aligned with each investor’s risk preferences. We estimate the efficient frontier accounting for random asset return variations and apply subsampling to derive confidence intervals for the distances. In our empirical illustration, we evaluate ‘decarbonized’ portfolios that exclude the most polluting firms from the S&P 500, considering four distinct investor types: those aiming to maximize return, minimize variance, maximize skewness, or balance these objectives. Results show that investors prioritizing return, skewness, or balanced criteria can decarbonize without significant efficiency loss. In contrast, those focused on minimizing variance face larger performance declines. Moreover, the portfolio closest to the efficient frontier varies according to investor preferences, highlighting the importance of personalizing performance metrics to individual investment goals. |
| Keywords: | Portfolio Choice; Personalization; Performance Measure; Random Inputs and Outputs; Nonparametric Estimator; Subsampling; Efficient Frontier |
| JEL: | C44 C12 C67 G11 G14 |
| Date: | 2025–10–03 |
| URL: | https://d.repec.org/n?u=RePEc:sol:wpaper:2013/394786 |
| By: | Leonardo D'Amico; Edward Glaeser; Joseph Gyourko; William Kerr; Giacomo A. M. Ponzetto |
| Abstract: | We document a Kuznets curve for construction productivity in 20th-century America. Homes built per construction worker remained stagnant between 1900 and 1940, boomed after World War II, and then plummeted after 1970. The productivity boom from 1940 to 1970 shows that nothing makes technological progress inherently impossible in construction. What stopped it? We present a model in which local land-use controls limit the size of building projects. This constraint reduces the equilibrium size of construction companies, reducing both scale economies and incentives to invest in innovation. Our model shows that, in a competitive industry, such inefficient reductions in firm size and technology investment are a distinctive consequence of restrictive project regulation, while classic regulatory barriers to entry increase firm size. The model is consistent with an extensive series of key facts about the nature of the construction sector. The post-1970 productivity decline coincides with increases in our best proxies for land-use regulation. The size of development projects is small today and has declined over time. The size of construction firms is also quite small, especially relative to other goods-producing firms, and smaller builders are less productive. Areas with stricter land use regulation have particularly small and unproductive construction establishments. Patenting activity in construction stagnated and diverged from other sectors. A back-of-the-envelope calculation indicates that, if half of the observed link between establishment size and productivity is causal, America’s residential construction firms would be approximately 60% more productive if their size distribution matched that of manufacturing. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:upf:upfgen:1896 |
| By: | Dragan Filimonovic; Christian Rutzer; Conny Wunsch |
| Abstract: | This paper estimates the effect of Generative AI (GenAI) adoption on scientific productivity and quality in the social and behavioral sciences. Using matched author-level panel data and a difference-in-differences design, we find that GenAI adoption is associated with sizable increases in research productivity, measured by the number of published papers. It also leads to moderate gains in publication quality, based on journal impact factors. These effects are most pronounced among early-career researchers, authors working in technically complex subfields, and those from non-English-speaking countries. The results suggest that GenAI tools may help lower some structural barriers in academic publishing and promote more inclusive participation in research. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.02408 |
| By: | Marcos Delprato |
| Abstract: | Latin America's education systems are fragmented and segregated, with substantial differences by school type. The concept of school efficiency (the ability of school to produce the maximum level of outputs given available resources) is policy relevant due to scarcity of resources in the region. Knowing whether private and public schools are making an efficient use of resources -- and which are the leading drivers of efficiency -- is critical, even more so after the learning crisis brought by the COVID-19 pandemic. In this paper, relying on data of 2, 034 schools and nine Latin American countries from PISA 2022, I offer new evidence on school efficiency (both on cognitive and non-cognitive dimensions) using Data Envelopment Analysis (DEA) by school type and, then, I estimate efficiency leading determinants through interpretable machine learning methods (IML). This hybrid DEA-IML approach allows to accommodate the issue of big data (jointly assessing several determinants of school efficiency). I find a cognitive efficiency gap of nearly 0.10 favouring private schools and of 0.045 for non-cognitive outcomes, and with a lower heterogeneity in private than public schools. For cognitive efficiency, leading determinants for the chance of a private school of being highly efficient are higher stock of books and PCs at home, lack of engagement in paid work and school's high autonomy; whereas low-efficient public schools are shaped by poor school climate, large rates of repetition, truancy and intensity of paid work, few books at home and increasing barriers for homework during the pandemic. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.25353 |
| By: | Arenas Díaz, Guillermo; Piva, Mariacristina; Vivarelli, Marco |
| Abstract: | This study investigates the relationship between Artificial Intelligence (AI) and innovation inputs in Spanish manufacturing firms. While AI is increasingly recognized as a driver of productivity and economic growth, its role in shaping firms’ innovation strategies remains underexplored. Using firm-level data, our analysis focuses on whether AI complements innovation inputs - specifically R&D and Embodied Technological Change (ETC) - and whether AI can be considered as a Method of Invention, able to trigger subsequent innovation investments. Results show a positive association between AI adoption and both internal R&D and ETC, in a static and a dynamic framework. Furthermore, empirical evidence also highlights heterogeneity, with important peculiarities affecting large vs small firms and high-tech vs low-tech companies. These findings suggest that AI may act as both a complement and a catalyst, depending on firm characteristics. |
| JEL: | O31 O32 |
| Date: | 2025–10–03 |
| URL: | https://d.repec.org/n?u=RePEc:unm:unumer:2025022 |
| By: | Kazuhiro Takauchi (Kansai University); Hajime Sugeta (Kansai University); Tomomichi Mizuno (Kobe University) |
| Abstract: | Real-world cost asymmetries highlight the importance of firm heterogeneity. Studies focus on monopolistic competition owing to model tractability but overlook oligopoly settings. We analyze how the productivity gap between efficient and inefficient oligopolistic firms affects export policies within a standard third-market model. We show that a "subsidy-tax-subsidy" export policy pattern emerges depending on the degree of the productivity gap. Extending our model to multiple firms, we consider whether the gap in firm numbers affects export policies. We find that when this gap is large, one exporter may receive an export subsidy whereas the other faces an export tax. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:koe:wpaper:2522 |
| By: | Andrew Foerster; Andreas Hornstein; Pierre-Daniel Sarte; Mark W. Watson |
| Abstract: | We explore the evolving significance of different production sectors within the U.S. economy since World War II and provide methods for estimating and forecasting these shifts. Using a compositional accounting approach, we find that the well-documented transition from goods to services is primarily driven by two compositional changes: 1) the rise of Intellectual Property Products (IPP) as an input producer, replacing Durable Goods almost one-for-one in terms of input shares in virtually all sectors; and 2) a shift in consumer spending from Nondurable Goods to Services. A structural model replicating these shifts reveals that the rise of IPP at the expense of Durable Goods is largely explained by increases in the efficiency of IPP inputs used in production: input-biased technical change. Trend variations in sectoral total factor productivity, and their attendant effects on relative prices and income, are the main driver of evolving consumption patterns. Both reduced-form and structural forecasts project these trends to continue over the next two decades, albeit at lower rates, indicating a slower pace of structural change. |
| JEL: | E17 E23 E27 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34338 |
| By: | Sid Kankanala |
| Abstract: | This paper develops a generalized (quasi-) Bayes framework for conditional moment restriction models, where the parameter of interest is a nonparametric structural function of endogenous variables. We establish contraction rates for a class of Gaussian process priors and provide conditions under which a Bernstein-von Mises theorem holds for the quasi-Bayes posterior. Consequently, we show that optimally weighted quasi-Bayes credible sets achieve exact asymptotic frequentist coverage, extending classical results for parametric GMM models. As an application, we estimate firm-level production functions using Chilean plant-level data. Simulations illustrate the favorable performance of generalized Bayes estimators relative to common alternatives. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.01036 |